Estimating linear temporal trends from aggregated environmental monitoring data

Trend estimates are often used as part of environmental monitoring programs. These trends inform managers (e.g., are desired species increasing or undesired species decreasing?). Data collected from environmental monitoring programs is often aggregated (i.e., averaged), which confounds sampling and process variation. State-space models allow sampling variation and process variations to be separated. We used simulated time-series to compare linear trend estimations from three state-space models, a simple linear regression model, and an auto-regressive model. We also compared the performance of these five models to estimate trends from a long term monitoring program. We specifically estimated trends for two species of fish and four species of aquatic vegetation from the Upper Mississippi River system. We found that the simple linear regression had the best performance of all the given models because it was best able to recover parameters and had consistent numerical convergence. Conversely, the simple linear regression did the worst job estimating populations in a given year. The state-space models did not estimate trends well, but estimated population sizes best when the models converged. We found that a simple linear regression performed better than more complex autoregression and state-space models when used to analyze aggregated environmental monitoring data.     

Effects of total flavonoids of raspberry on perimenopausal model in mice

To study the effect of raspberry total flavonoids on perimenopausal model in mice. Blank group, sham operation, and the rest of the mice made the menopausal model. Choose 72 mice castrated completely random divided into 6 groups for the experiment, respectively: model group, gengnianan (GNA) capsule group, soybean isoflavone soft (SIS) capsule group, high, mid and low dose group of total flavonoids of raspberry (TFR). Animals in each group were given the corresponding drugs tenth days after operation, and were given intragastrical administration of once a day for continuous administration of 21 days. Each group of mice in the administration of 18 days to determine the number of autonomic activities within 5 min, in the administration of 19–20 days to determine the incubation of the mice first entry into the darkroom and the number of shocks into the darkroom within 5 min. At 2 h after the last administration (fasting for 12 h), mice were sacrificed and serum was collected. Serum levels of E2, T, LH and FSH were measured. Dissect the uterus, uterus, thymus and spleen. Weigh the wet weight and calculate the organ index, the morphological changes of uterus, thymus and spleen were observed. The results showed that the TFR had a good therapeutic effect on the perimenopausal model of mice after giving a high, mid and low dose of raspberry flavonoids for some time.     

Identification of a four-gene metabolic signature predicting overall survival for hepatocellular carcinoma

While hundreds of consistently altered metabolic genes had been identified in hepatocellular carcinoma (HCC), the prognostic role of them remains to be further elucidated. Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas—Liver Hepatocellular Carcinoma and GSE14520 data set from the Gene Expression Omnibus database. Univariate Cox regression analysis and lasso Cox regression model established a novel four-gene metabolic signature (including acetyl-CoA acetyltransferase 1, glutamic-oxaloacetic transaminase 2, phosphatidylserine synthase 2, and uridine-cytidine kinase 2) for HCC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. The signature was significantly correlated with other negative prognostic factors such as higher α-fetoprotein. The signature was found to be an independent prognostic factor for HCC survival. Nomogram including the signature shown some clinical net benefit for overall survival prediction. Furthermore, gene set enrichment analyses revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four-gene metabolic signature for HCC prognosis prediction. The signature might reflect the dysregulated metabolic microenvironment and provided potential biomarkers for metabolic therapy and treatment response prediction in HCC.     

An Efficient Timer and Sizer of Biomacromolecular Motions

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CyberKnife beam output factor measurements: A multi-site and multi-detector study

PurposeNew promising detectors are available for measuring small field size output factors (OFs). This study focused on a multicenter evaluation of two new generation detectors for OF measurements on CyberKnife systems.MethodsPTW-60019 microDiamond and W1 plastic scintillation detector (PSD) were used to measure OFs on eight CyberKnife units of various generations for 5–60 mm fixed cones. MicroDiamond and PSD OF were compared to routinely used silicon diodes data corrected applying published Monte Carlo (MC) factors. PSD data were corrected for Čerenkov Light Ratio (CLR). The uncertainties related to CLR determination were estimated.ResultsConsidering OF values averaged over all centers, the differences between MC corrected diode and the other two detectors were within 1.5%. MicroDiamond exhibited an over-response of 1.3% at 7.5 mm and a trend inversion at 5 mm with a difference of 0.2%. This behavior was consistent among the different units. OFs measured by PSD slightly under-responded compared to MC corrected diode for the smaller cones and the differences were within 1%. The observed CLR variability was 2.5% and the related variation in OF values was 1.9%.ConclusionThis study indicates that CyberKnife microDiamond OF require corrections below 2%. The results are enhanced by the consistency observed among different units. Scintillator shows a good agreement to MC corrected diode but CLR determination remains critical requiring further investigations. The results emphasized the value of a multi-center validation over a single center approach.     

An iterative learning strategy for the auto-tuning of the feedforward and feedback controller in type-1 diabetes

This paper proposes a scheme for the control of the blood glucose in subjects with type-1 diabetes mellitus based on the subcutaneous (s.c.) glucose measurement and s.c. insulin administration. The tuning of the controller is based on an iterative learning strategy that exploits the repetitiveness of the daily feeding habit of a patient. The control consists of a mixed feedback and feedforward contribution whose parameters are tuned through an iterative learning process that is based on the day-by-day automated analysis of the glucose response to the infusion of exogenous insulin. The scheme does not require any a priori information on the patient insulin/glucose response, on the meal times and on the amount of ingested carbohydrates (CHOs). Thanks to the learning mechanism the scheme is able to improve its performance over time. A specific logic is also introduced for the detection and prevention of possible hypoglycaemia events. The effectiveness of the methodology has been validated using long-term simulation studies applied to a set of nine in silico patients considering realistic uncertainties on the meal times and on the quantities of ingested CHOs.     

Computational approaches from polymer physics to investigate chromatin folding

Microscopy and sequencing-based technologies are providing increasing insights into chromatin architecture. Nevertheless, a full comprehension of chromosome folding and its link with vital cell functions is far from accomplished at the molecular level. Recent theoretical and computational approaches are providing important support to experiments to dissect the three-dimensional structure of chromosomes and its organizational mechanisms. Here, we review, in particular, the String&Binders polymer model of chromatin that describes the textbook scenario where contacts between distal DNA sites are established by cognate binders. It has been shown to recapitulate key features of chromosome folding and to be able at predicting how phenotypes causing structural variants rewire the interactions between genes and regulators.